BOL: Related items

Genome Assembly Workshop 2020

Jit — Wed, 25 Aug 2021 04:30:32 -0500

Our team offers custom bioinformatics services to academic and private organizations. We have a strong academic background with a focus on cutting edge, open source software. We replicate standard analysis pipelines (best practices) when appropriate, and/or develop novel applications and pipelines when needed, however we always emphasize biological interpretation of the data.

More at https://ucdavis-bioinformatics-training.github.io/

Address of the bookmark: https://ucdavis-bioinformatics-training.github.io/2020-Genome_Assembly_Workshop/snakemake/snakemake_intro

ALE: Assembly Likelihood Estimator

BioStar — Wed, 08 Mar 2023 01:39:33 -0600

Just import the assembly, bam and ALE scores. You can convert the .ale file to a set of .wig files with ale2wiggle.py and IGV can read those directly. Depending on your genome size you may want to convert the .wig files to the BigWig format.

Address of the bookmark: https://github.com/sc932/ALE

MetaGraph: Ultra Scalable Framework for DNA Search, Alignment, Assembly

Abhi — Sat, 08 Jun 2024 16:15:25 -0500

The MetaGraph framework is designed to work with a wide range of input data sets, indexing from a few samples up to the contents of entire archives with hundreds of thousands of records. The indexing workflow always follows the same principle, transforming single input samples into error-removed, refined sample graphs, which are then merged into a joint metagraph index. Each input sample is annotated in the joint index as a subgraph. This graph index enriched with metadata can then be used for downstream applications such as sequence search or differential assembly.

Searcg link https://metagraph.ethz.ch/search

Pre-print https://www.biorxiv.org/content/10.1101/2020.10.01.322164v4

Address of the bookmark: https://metagraph.ethz.ch/

LRCstats: a tool for evaluating long reads correction methods

Aaryan Lokwani — Wed, 22 Aug 2018 11:05:04 -0500

LRCstats is an open-source pipeline for benchmarking DNA long read correction algorithms for long reads outputted by third generation sequencing technology such as machines produced by Pacific Biosciences. The reads produced by third generation sequencing technology, as the name suggests, are longer in length than reads produced by next generation sequencing technologies, such as those produced by Illumina. However, long reads are plagued by high error rates, which can cause issues in downstream analysis. Long read correction algorithms reduce the error rate of long reads either through self-correcting methods or using accurate, short reads outputted by next generation sequencing technologies to correct long reads.

Address of the bookmark: https://github.com/cchauve/lrcstats

SEASTAR: Systematic Evaluation of Alternative STArt site in RNA

BioStar — Thu, 13 Aug 2020 09:54:27 -0500

SEASTAR (Systematic Evaluation of Alternative STArt site in RNA) is a software package for Transcription Start Site (TSS) identification and quantification using only RNA-seq data. It assembles novel TSSs based only on RNA-Seq data and merges them with known TSSs from a public database. This package enables high-quality TSS identification that is comparable to the highly sophisticated CAGE technology. This package is particularly useful for finding novel TSSs that contribute to transcriptome complexity along with identifying differential promoter utilization.

version 1.0.0 - updates several descriptions and tests. To achieve v0.9.4, one can visit https://github.com/zhyqin/SEASTAR-0.9.4 for download.

Address of the bookmark: https://github.com/Xinglab/SEASTAR

IgBLAST 1.17 is now available with improved identification of productive V gene sequences

Jit — Sun, 01 Nov 2020 16:52:58 -0600

A new release of IgBLAST (1.17), the popular package for classifying and analyzing immunoglobulin and T cell receptor sequences, is now available on the web and from the FTP site. The updated package is better at identifying productive V gene sequences. We added a new field , “V frame shift”, to the IgBLAST output to indicate whether the V gene translation frame contains a frame-shift. We have also updated the definition of a productive V(D)J sequence to now exclude those with internal frame shifts.

See the new IgBLAST manual on the NCBI GitHub site for more information on setting up and running IgBLAST.

If you have any questions or concerns, please email us at blast-help@ncbi.nlm.nih.gov

Understanding kmer !

BioStar — Wed, 18 Aug 2021 04:27:51 -0500

What is a k-mer anyway? A k-mer is just a sequence of k characters in a string (or nucleotides in a DNA sequence). Now, it is important to remember that to get all k-mers from a sequence you need to get the first k characters, then move just a single character for the start of the next k-mer and so on. Effectively, this will create sequences that overlap in k-1 positions.

Address of the bookmark: https://bioinfologics.github.io/post/2018/09/17/k-mer-counting-part-i-introduction/

KAD: Assessing genome assemblies using K-mer copies in assemblies and K-mer abundance in Illumina reads

Jit — Fri, 19 Jun 2020 07:34:12 -0500

KAD is designed for evaluating the accuracy of nucleotide base quality of genome assemblies. Briefly, abundance of k-mers are quantified for both sequencing reads and assembly sequences. Comparison of the two values results in a single value per k-mer, K-mer Abundance Difference (KAD), which indicates how well the assembly matches read data for each k-mer.

where, c is the count of a k-mer from reads, m is the mode of counts of read k-mers, and n is the copy of the k-mer in the assembly.

Address of the bookmark: https://github.com/liu3zhenlab/KAD

You and your friend have similar DNA !!!

Jit — Sun, 27 Jul 2014 20:44:05 -0500

New research out of Massachusetts claims that people often choose friends that are similar to them in genetics and they are more accurate than you might suppose. A study published on PNAS http://www.pnas.org/content/111/Supplement_3/10796.full found that people are apt to pick friends who are genetically similar to themselves - so much so that friends tend to be as alike at the genetic level as a person's fourth cousin.

Scientists with a long-running Framingham Heart Study looked at 1,932 people (examination of about 1.5 million markers of genetic variations), comparing unrelated friends to unrelated strangers. They found that friends shared about 1% of their genes — a percentage much higher than those shared with strangers.This new findings made it clear that people have more DNA in common with those who are selected as friends than with strangers in the same population.

The genes that lined up the most were olfactory genes, which deal with smell. The ones that lined up the least were immune system genes. The researchers weren't sure why that happened :/. Olfactory genes might be a straightforward explanation: People who like the same smells tend to be drawn to similar environments, where they meet others with the same tendencies.

Reference:

http://www.pnas.org/content/111/Supplement_3/10796.full

Image : http://i.kinja-img.com

Calling variants in non-diploid systems

Neel — Sat, 26 Jun 2021 15:37:49 -0500

The main challenge associated with non-diploid variant calling is the difficulty in distinguishing between the sequencing noise (abundant in all NGS platforms) and true low frequency variants. Some of the early attempts to do this well have been accomplished on human mitochondrial DNA although the same approaches will work equally good on viral and bacterial genomes (Rebolledo-Jaramillo et al. 2014, Li et al. 2015).

Address of the bookmark: https://training.galaxyproject.org/training-material/topics/variant-analysis/tutorials/non-dip/tutorial.html